Overview

Dataset statistics

Number of variables15
Number of observations252
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.7 KiB
Average record size in memory120.5 B

Variable types

NUM15

Reproduction

Analysis started2020-08-25 00:04:07.019554
Analysis finished2020-08-25 00:04:38.813014
Duration31.79 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Abdomen is highly correlated with ChestHigh correlation
Chest is highly correlated with AbdomenHigh correlation
Hip is highly correlated with WeightHigh correlation
Weight is highly correlated with HipHigh correlation
target is highly correlated with DensityHigh correlation
Density is highly correlated with targetHigh correlation

Variables

Density
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count218
Unique (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.055573810187597
Minimum0.9950000047683716
Maximum1.10889995098114
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:38.860292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.9950000048
5-th percentile1.025709975
Q11.041399956
median1.05490005
Q31.0704
95-th percentile1.085344976
Maximum1.108899951
Range0.1138999462
Interquartile range (IQR)0.02900004387

Descriptive statistics

Standard deviation0.01903143025
Coefficient of variation (CV)0.01802946422
Kurtosis-0.309619783
Mean1.05557381
Median Absolute Deviation (MAD)0.01404994726
Skewness-0.0201763089
Sum266.0046002
Variance0.0003621953372
2020-08-25T00:04:38.963421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.0609999941.6%
 
1.04139995631.2%
 
1.04840004431.2%
 
1.05239999331.2%
 
1.05869996520.8%
 
1.04030001220.8%
 
1.02499997620.8%
 
1.04620003720.8%
 
1.04770004720.8%
 
1.03729999120.8%
 
1.03989994520.8%
 
1.06659996520.8%
 
1.0549000520.8%
 
1.07749998620.8%
 
1.06780004520.8%
 
1.04240000220.8%
 
1.06480002420.8%
 
1.05200004620.8%
 
1.03400003920.8%
 
1.06029999320.8%
 
1.05429995120.8%
 
1.03840005420.8%
 
1.05019998620.8%
 
1.05750000520.8%
 
1.070420.8%
 
Other values (193)19778.2%
 
ValueCountFrequency (%) 
0.995000004810.4%
 
1.01010000710.4%
 
1.01400005810.4%
 
1.01800000710.4%
 
1.02020001410.4%
 
1.02069997810.4%
 
1.02090001110.4%
 
1.02170002510.4%
 
1.02359998210.4%
 
1.02499997620.8%
 
ValueCountFrequency (%) 
1.10889995110.4%
 
1.09909999410.4%
 
1.0982999810.4%
 
1.09259998810.4%
 
1.09109997710.4%
 
1.09099996110.4%
 
1.09060001410.4%
 
1.09029996410.4%
 
1.09000003310.4%
 
1.0873999610.4%
 

Age
Real number (ℝ≥0)

Distinct count51
Unique (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.88492063492063
Minimum22.0
Maximum81.0
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:39.232501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile25
Q135.75
median43
Q354
95-th percentile67
Maximum81
Range59
Interquartile range (IQR)18.25

Descriptive statistics

Standard deviation12.60203972
Coefficient of variation (CV)0.2807633286
Kurtosis-0.4164409143
Mean44.88492063
Median Absolute Deviation (MAD)8
Skewness0.2835210973
Sum11311
Variance158.8114052
2020-08-25T00:04:39.342936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
40176.7%
 
43135.2%
 
42124.8%
 
47114.4%
 
35104.0%
 
55104.0%
 
41104.0%
 
4493.6%
 
4993.6%
 
5483.2%
 
2872.8%
 
2772.8%
 
5072.8%
 
4662.4%
 
2662.4%
 
6252.0%
 
3952.0%
 
4852.0%
 
5152.0%
 
7252.0%
 
2441.6%
 
3441.6%
 
2541.6%
 
3141.6%
 
3241.6%
 
Other values (26)6525.8%
 
ValueCountFrequency (%) 
2220.8%
 
2341.6%
 
2441.6%
 
2541.6%
 
2662.4%
 
2772.8%
 
2872.8%
 
2920.8%
 
3020.8%
 
3141.6%
 
ValueCountFrequency (%) 
8110.4%
 
7410.4%
 
7252.0%
 
7020.8%
 
6920.8%
 
6810.4%
 
6741.6%
 
6620.8%
 
6531.2%
 
6441.6%
 

Weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count197
Unique (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.92440468924386
Minimum118.5
Maximum363.1499938964844
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:39.453954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum118.5
5-th percentile136.3875
Q1159
median176.5
Q3197
95-th percentile225.65
Maximum363.1499939
Range244.6499939
Interquartile range (IQR)38

Descriptive statistics

Standard deviation29.38915969
Coefficient of variation (CV)0.1642546177
Kurtosis5.269513004
Mean178.9244047
Median Absolute Deviation (MAD)19.375
Skewness1.205262861
Sum45088.94998
Variance863.7227074
2020-08-25T00:04:39.552922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
16731.2%
 
184.2531.2%
 
170.7531.2%
 
177.2531.2%
 
152.2531.2%
 
179.7531.2%
 
16831.2%
 
172.7531.2%
 
168.2531.2%
 
200.2520.8%
 
198.520.8%
 
22320.8%
 
171.2520.8%
 
127.520.8%
 
155.2520.8%
 
206.520.8%
 
178.2520.8%
 
202.2520.8%
 
160.7520.8%
 
159.2520.8%
 
156.7520.8%
 
167.520.8%
 
154.520.8%
 
162.7520.8%
 
162.520.8%
 
Other values (172)19376.6%
 
ValueCountFrequency (%) 
118.510.4%
 
12510.4%
 
125.2510.4%
 
125.7510.4%
 
126.510.4%
 
127.520.8%
 
131.510.4%
 
133.2510.4%
 
133.510.4%
 
134.2510.4%
 
ValueCountFrequency (%) 
363.149993910.4%
 
262.7510.4%
 
247.2510.4%
 
244.2510.4%
 
241.7510.4%
 
241.2510.4%
 
234.7510.4%
 
234.2510.4%
 
232.7510.4%
 
23010.4%
 

Height
Real number (ℝ≥0)

Distinct count48
Unique (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.14880952380952
Minimum29.5
Maximum77.75
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:39.665075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum29.5
5-th percentile65.8875
Q168.25
median70
Q372.25
95-th percentile74.5
Maximum77.75
Range48.25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.662855788
Coefficient of variation (CV)0.0522155089
Kurtosis59.54430209
Mean70.14880952
Median Absolute Deviation (MAD)2
Skewness-5.384986544
Sum17677.5
Variance13.41651252
2020-08-25T00:04:39.771410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
71.5145.6%
 
69.5124.8%
 
72.25124.8%
 
69.25124.8%
 
67.5114.4%
 
70104.0%
 
69.7593.6%
 
67.7583.2%
 
70.572.8%
 
71.2572.8%
 
68.7572.8%
 
68.2572.8%
 
68.572.8%
 
73.572.8%
 
7272.8%
 
71.7562.4%
 
6862.4%
 
70.7562.4%
 
65.7562.4%
 
70.2562.4%
 
67.2562.4%
 
72.7562.4%
 
74.562.4%
 
6952.0%
 
72.552.0%
 
Other values (23)5722.6%
 
ValueCountFrequency (%) 
29.510.4%
 
6420.8%
 
64.7510.4%
 
6510.4%
 
65.520.8%
 
65.7562.4%
 
6620.8%
 
66.2510.4%
 
66.520.8%
 
66.7541.6%
 
ValueCountFrequency (%) 
77.7510.4%
 
77.510.4%
 
7620.8%
 
75.510.4%
 
75.2510.4%
 
7510.4%
 
74.7520.8%
 
74.562.4%
 
74.2552.0%
 
7441.6%
 

Neck
Real number (ℝ≥0)

Distinct count90
Unique (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.99206362073384
Minimum31.10000038146973
Maximum51.20000076293945
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:39.880241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum31.10000038
5-th percentile34.25499992
Q136.40000153
median38
Q339.42500114
95-th percentile41.84500027
Maximum51.20000076
Range20.10000038
Interquartile range (IQR)3.024999619

Descriptive statistics

Standard deviation2.430913225
Coefficient of variation (CV)0.06398476401
Kurtosis2.719615815
Mean37.99206362
Median Absolute Deviation (MAD)1.599998474
Skewness0.5526199398
Sum9574.000032
Variance5.909339108
2020-08-25T00:04:39.979044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
38.5124.8%
 
38104.0%
 
37.4000015383.2%
 
38.7000007672.8%
 
36.572.8%
 
37.7999992472.8%
 
35.562.4%
 
40.7999992462.4%
 
36.2999992452.0%
 
38.2999992452.0%
 
38.4000015352.0%
 
42.0999984752.0%
 
40.2000007652.0%
 
37.552.0%
 
36.4000015352.0%
 
39.4000015352.0%
 
38.0999984741.6%
 
37.9000015341.6%
 
36.9000015341.6%
 
35.2000007641.6%
 
3741.6%
 
40.7000007641.6%
 
38.9000015341.6%
 
3641.6%
 
37.7000007641.6%
 
Other values (65)11344.8%
 
ValueCountFrequency (%) 
31.1000003810.4%
 
31.510.4%
 
32.7999992410.4%
 
33.2000007610.4%
 
33.4000015310.4%
 
33.5999984710.4%
 
33.7999992410.4%
 
33.9000015310.4%
 
3431.2%
 
34.0999984710.4%
 
ValueCountFrequency (%) 
51.2000007610.4%
 
43.9000015310.4%
 
43.2000007610.4%
 
42.7999992410.4%
 
42.510.4%
 
42.0999984752.0%
 
4210.4%
 
41.9000015320.8%
 
41.7999992420.8%
 
41.520.8%
 

Chest
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count174
Unique (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.82420621599469
Minimum79.30000305175781
Maximum136.19999694824222
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:40.090828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum79.30000305
5-th percentile89.01999969
Q194.35000038
median99.64999771
Q3105.3750019
95-th percentile116.3400017
Maximum136.1999969
Range56.8999939
Interquartile range (IQR)11.02500153

Descriptive statistics

Standard deviation8.430475619
Coefficient of variation (CV)0.08361559129
Kurtosis0.9872818955
Mean100.8242062
Median Absolute Deviation (MAD)5.649997711
Skewness0.6815556539
Sum25407.69997
Variance71.07291917
2020-08-25T00:04:40.204211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
102.699996952.0%
 
99.0999984752.0%
 
9441.6%
 
97.8000030541.6%
 
99.5999984741.6%
 
98.9000015341.6%
 
89.1999969531.2%
 
92.3000030531.2%
 
105.599998531.2%
 
92.9000015331.2%
 
101.800003131.2%
 
93.531.2%
 
97.4000015331.2%
 
107.599998531.2%
 
105.300003131.2%
 
10231.2%
 
10431.2%
 
91.5999984720.8%
 
97.3000030520.8%
 
92.1999969520.8%
 
93.3000030520.8%
 
104.300003120.8%
 
101.900001520.8%
 
103.699996920.8%
 
99.1999969520.8%
 
Other values (149)17770.2%
 
ValueCountFrequency (%) 
79.3000030510.4%
 
83.4000015310.4%
 
85.0999984710.4%
 
8610.4%
 
86.6999969510.4%
 
87.5999984710.4%
 
87.6999969510.4%
 
88.1999969510.4%
 
88.520.8%
 
88.5999984710.4%
 
ValueCountFrequency (%) 
136.199996910.4%
 
128.300003110.4%
 
121.599998510.4%
 
119.900001510.4%
 
119.800003110.4%
 
119.699996910.4%
 
119.599998510.4%
 
119.199996910.4%
 
118.510.4%
 
118.300003110.4%
 

Abdomen
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count185
Unique (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.55595240517268
Minimum69.40000152587889
Maximum148.10000610351562
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:40.318012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum69.40000153
5-th percentile76.87499847
Q184.57499886
median90.95000076
Q399.32499695
95-th percentile110.7599995
Maximum148.1000061
Range78.70000458
Interquartile range (IQR)14.74999809

Descriptive statistics

Standard deviation10.783077
Coefficient of variation (CV)0.1165033336
Kurtosis2.248824452
Mean92.55595241
Median Absolute Deviation (MAD)7.350002289
Skewness0.8384180224
Sum23324.10001
Variance116.2747497
2020-08-25T00:04:40.425555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
89.6999969541.6%
 
100.541.6%
 
88.6999969541.6%
 
83.5999984731.2%
 
82.8000030531.2%
 
10531.2%
 
98.5999984731.2%
 
9531.2%
 
92.4000015331.2%
 
90.3000030531.2%
 
99.8000030531.2%
 
95.5999984731.2%
 
10031.2%
 
88.0999984720.8%
 
88.1999969520.8%
 
86.5999984720.8%
 
77.5999984720.8%
 
79.4000015320.8%
 
101.300003120.8%
 
86.4000015320.8%
 
89.5999984720.8%
 
86.0999984720.8%
 
91.5999984720.8%
 
90.9000015320.8%
 
87.5999984720.8%
 
Other values (160)18673.8%
 
ValueCountFrequency (%) 
69.4000015310.4%
 
70.4000015310.4%
 
72.8000030510.4%
 
73.6999969510.4%
 
73.9000015310.4%
 
74.5999984710.4%
 
7510.4%
 
7620.8%
 
76.3000030510.4%
 
76.4000015310.4%
 
ValueCountFrequency (%) 
148.100006110.4%
 
126.199996910.4%
 
122.099998510.4%
 
11810.4%
 
115.900001510.4%
 
115.599998510.4%
 
113.900001510.4%
 
113.800003110.4%
 
113.699996910.4%
 
113.400001510.4%
 

Hip
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count152
Unique (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.90476184421115
Minimum85.0
Maximum147.6999969482422
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:40.549652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile89.15499763
Q195.5
median99.30000305
Q3103.5249996
95-th percentile112.1250015
Maximum147.6999969
Range62.69999695
Interquartile range (IQR)8.024999619

Descriptive statistics

Standard deviation7.164057742
Coefficient of variation (CV)0.07170887163
Kurtosis7.471349889
Mean99.90476184
Median Absolute Deviation (MAD)3.899997711
Skewness1.497127145
Sum25175.99998
Variance51.32372333
2020-08-25T00:04:40.652740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
98.3000030572.8%
 
100.599998562.4%
 
99.3000030552.0%
 
96.1999969552.0%
 
94.541.6%
 
102.541.6%
 
9431.2%
 
97.0999984731.2%
 
104.099998531.2%
 
99.1999969531.2%
 
99.5999984731.2%
 
98.6999969531.2%
 
96.0999984731.2%
 
101.599998531.2%
 
96.9000015331.2%
 
95.5999984731.2%
 
101.699996931.2%
 
97.8000030531.2%
 
95.520.8%
 
88.5999984720.8%
 
100.699996920.8%
 
95.1999969520.8%
 
96.4000015320.8%
 
108.800003120.8%
 
98.4000015320.8%
 
Other values (127)17167.9%
 
ValueCountFrequency (%) 
8510.4%
 
85.3000030510.4%
 
87.1999969510.4%
 
87.510.4%
 
87.5999984710.4%
 
88.1999969510.4%
 
88.520.8%
 
88.5999984720.8%
 
88.8000030510.4%
 
8910.4%
 
ValueCountFrequency (%) 
147.699996910.4%
 
125.599998510.4%
 
116.099998510.4%
 
115.510.4%
 
114.400001520.8%
 
114.300003110.4%
 
114.099998510.4%
 
113.900001520.8%
 
113.800003110.4%
 
112.800003110.4%
 

Thigh
Real number (ℝ≥0)

Distinct count139
Unique (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.40595234764947
Minimum47.20000076293945
Maximum87.30000305175781
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:40.762356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum47.20000076
5-th percentile51.15499973
Q156
median59
Q362.34999943
95-th percentile68.54499931
Maximum87.30000305
Range40.10000229
Interquartile range (IQR)6.349999428

Descriptive statistics

Standard deviation5.249952035
Coefficient of variation (CV)0.0883741751
Kurtosis2.66571581
Mean59.40595235
Median Absolute Deviation (MAD)3.099998474
Skewness0.8212097089
Sum14970.29999
Variance27.56199637
2020-08-25T00:04:40.869774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
58.9000015372.8%
 
59.2999992452.0%
 
57.552.0%
 
58.552.0%
 
54.7000007652.0%
 
5652.0%
 
57.4000015341.6%
 
60.5999984741.6%
 
6041.6%
 
63.541.6%
 
54.2999992441.6%
 
58.4000015341.6%
 
59.0999984741.6%
 
57.0999984741.6%
 
63.7000007631.2%
 
61.2000007631.2%
 
63.4000015331.2%
 
57.2999992431.2%
 
62.0999984731.2%
 
64.8000030531.2%
 
56.7999992431.2%
 
55.531.2%
 
60.7000007631.2%
 
63.2999992431.2%
 
5531.2%
 
Other values (114)15561.5%
 
ValueCountFrequency (%) 
47.2000007610.4%
 
49.2999992410.4%
 
49.5999984710.4%
 
5020.8%
 
50.0999984720.8%
 
50.2999992410.4%
 
50.5999984710.4%
 
50.7000007610.4%
 
50.7999992410.4%
 
50.9000015310.4%
 
ValueCountFrequency (%) 
87.3000030510.4%
 
74.4000015310.4%
 
72.9000015310.4%
 
72.510.4%
 
71.1999969520.8%
 
70.5999984710.4%
 
69.8000030510.4%
 
69.510.4%
 
69.1999969510.4%
 
6910.4%
 

Knee
Real number (ℝ≥0)

Distinct count90
Unique (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.59047614203559
Minimum33.0
Maximum49.099998474121094
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:40.990994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile34.79999924
Q136.97500038
median38.5
Q339.92500114
95-th percentile42.6449995
Maximum49.09999847
Range16.09999847
Interquartile range (IQR)2.950000763

Descriptive statistics

Standard deviation2.411804489
Coefficient of variation (CV)0.06249740168
Kurtosis1.061535308
Mean38.59047614
Median Absolute Deviation (MAD)1.5
Skewness0.5167439978
Sum9724.799988
Variance5.816800891
2020-08-25T00:04:41.107411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3993.6%
 
38.0999984772.8%
 
37.2999992472.8%
 
38.2999992472.8%
 
3872.8%
 
38.4000015362.4%
 
38.7000007662.4%
 
36.2000007662.4%
 
39.4000015362.4%
 
4062.4%
 
38.2000007652.0%
 
38.7999992452.0%
 
39.2000007652.0%
 
39.2999992441.6%
 
36.541.6%
 
34.7999992441.6%
 
38.5999984741.6%
 
37.5999984741.6%
 
39.5999984741.6%
 
39.7999992441.6%
 
40.5999984741.6%
 
36.0999984741.6%
 
37.541.6%
 
37.4000015341.6%
 
39.7000007641.6%
 
Other values (65)12248.4%
 
ValueCountFrequency (%) 
3310.4%
 
33.4000015310.4%
 
33.510.4%
 
33.7000007610.4%
 
34.2000007610.4%
 
34.4000015310.4%
 
34.520.8%
 
34.7000007620.8%
 
34.7999992441.6%
 
34.9000015320.8%
 
ValueCountFrequency (%) 
49.0999984710.4%
 
4610.4%
 
4510.4%
 
44.2000007610.4%
 
4410.4%
 
43.520.8%
 
43.2999992420.8%
 
43.0999984710.4%
 
42.7999992410.4%
 
42.7000007620.8%
 

Ankle
Real number (ℝ≥0)

Distinct count61
Unique (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.10238097962879
Minimum19.100000381469727
Maximum33.900001525878906
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:41.226992image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum19.10000038
5-th percentile21
Q122
median22.79999924
Q324
95-th percentile25.44499979
Maximum33.90000153
Range14.80000114
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.694893501
Coefficient of variation (CV)0.07336445115
Kurtosis11.94519641
Mean23.10238098
Median Absolute Deviation (MAD)0.8999996185
Skewness2.255134555
Sum5821.800007
Variance2.872663979
2020-08-25T00:04:41.333865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
22.5114.4%
 
22114.4%
 
22.60000038114.4%
 
21.79999924104.0%
 
23.2000007693.6%
 
22.7000007693.6%
 
2483.2%
 
21.583.2%
 
22.3999996283.2%
 
24.6000003883.2%
 
23.3999996283.2%
 
21.8999996283.2%
 
22.8999996272.8%
 
22.1000003872.8%
 
24.7000007672.8%
 
22.2999992472.8%
 
24.1000003862.4%
 
2162.4%
 
2362.4%
 
24.7999992452.0%
 
23.1000003852.0%
 
23.7999992452.0%
 
22.7999992452.0%
 
23.6000003841.6%
 
23.541.6%
 
Other values (36)6927.4%
 
ValueCountFrequency (%) 
19.1000003810.4%
 
19.7000007610.4%
 
20.1000003810.4%
 
20.2000007610.4%
 
20.3999996220.8%
 
20.510.4%
 
20.6000003810.4%
 
20.7999992410.4%
 
20.8999996210.4%
 
2162.4%
 
ValueCountFrequency (%) 
33.9000015310.4%
 
33.7000007610.4%
 
29.6000003810.4%
 
2710.4%
 
26.6000003810.4%
 
26.2999992410.4%
 
2610.4%
 
25.8999996210.4%
 
25.7999992410.4%
 
25.6000003820.8%
 

Biceps
Real number (ℝ≥0)

Distinct count104
Unique (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.27341265905471
Minimum24.79999923706055
Maximum45.0
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:41.445994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum24.79999924
5-th percentile27.61000042
Q130.20000076
median32.04999924
Q334.32499981
95-th percentile37.20000076
Maximum45
Range20.20000076
Interquartile range (IQR)4.124999046

Descriptive statistics

Standard deviation3.021273781
Coefficient of variation (CV)0.09361494592
Kurtosis0.498498406
Mean32.27341266
Median Absolute Deviation (MAD)1.949999809
Skewness0.2855299565
Sum8132.89999
Variance9.128095258
2020-08-25T00:04:41.552090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
30.572.8%
 
31.6000003872.8%
 
31.3999996252.0%
 
32.552.0%
 
3152.0%
 
30.1000003852.0%
 
30.2999992441.6%
 
35.0999984741.6%
 
27.8999996241.6%
 
33.5999984741.6%
 
31.7000007641.6%
 
32.9000015341.6%
 
29.3999996241.6%
 
35.5999984741.6%
 
30.6000003841.6%
 
32.0999984741.6%
 
28.7999992441.6%
 
35.2999992441.6%
 
32.4000015341.6%
 
31.2999992441.6%
 
33.541.6%
 
37.2000007641.6%
 
33.2999992441.6%
 
29.2000007631.2%
 
29.7000007631.2%
 
Other values (79)14457.1%
 
ValueCountFrequency (%) 
24.7999992410.4%
 
25.2999992410.4%
 
25.6000003810.4%
 
25.7999992410.4%
 
2610.4%
 
26.1000003810.4%
 
26.7000007610.4%
 
26.7999992410.4%
 
2720.8%
 
27.2999992410.4%
 
ValueCountFrequency (%) 
4510.4%
 
39.0999984710.4%
 
38.520.8%
 
38.4000015310.4%
 
38.2000007610.4%
 
37.7000007610.4%
 
37.520.8%
 
37.2999992420.8%
 
37.2000007641.6%
 
37.0999984720.8%
 

Forearm
Real number (ℝ≥0)

Distinct count77
Unique (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.663888878292507
Minimum21.0
Maximum34.900001525878906
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:41.675596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile25.70000076
Q127.29999924
median28.70000076
Q330
95-th percentile31.74500008
Maximum34.90000153
Range13.90000153
Interquartile range (IQR)2.700000763

Descriptive statistics

Standard deviation2.020691175
Coefficient of variation (CV)0.07049605806
Kurtosis0.8663093674
Mean28.66388888
Median Absolute Deviation (MAD)1.399999619
Skewness-0.2193326386
Sum7223.299997
Variance4.083192823
2020-08-25T00:04:41.779785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
29.6000003893.6%
 
27.2999992493.6%
 
29.7999992493.6%
 
27.3999996272.8%
 
29.2000007672.8%
 
3062.4%
 
26.2999992462.4%
 
28.3999996262.4%
 
30.1000003862.4%
 
30.2999992452.0%
 
28.7000007652.0%
 
27.7000007652.0%
 
27.7999992452.0%
 
28.2999992452.0%
 
2952.0%
 
28.2000007652.0%
 
2852.0%
 
29.2999992452.0%
 
27.552.0%
 
26.2000007641.6%
 
25.7000007641.6%
 
27.2000007641.6%
 
30.7000007641.6%
 
29.3999996241.6%
 
28.8999996241.6%
 
Other values (52)11344.8%
 
ValueCountFrequency (%) 
2110.4%
 
2210.4%
 
23.1000003820.8%
 
24.6000003810.4%
 
24.7999992410.4%
 
25.2000007631.2%
 
25.510.4%
 
25.7000007641.6%
 
25.7999992420.8%
 
25.8999996231.2%
 
ValueCountFrequency (%) 
34.9000015310.4%
 
33.7999992410.4%
 
33.7000007610.4%
 
33.0999984710.4%
 
32.7999992410.4%
 
32.7000007610.4%
 
32.5999984710.4%
 
32.510.4%
 
32.4000015310.4%
 
3210.4%
 

Wrist
Real number (ℝ≥0)

Distinct count44
Unique (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.22976189189487
Minimum15.800000190734865
Maximum21.399999618530273
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:04:41.891123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum15.80000019
5-th percentile16.79999924
Q117.60000038
median18.29999924
Q318.79999924
95-th percentile19.79999924
Maximum21.39999962
Range5.599999428
Interquartile range (IQR)1.199998856

Descriptive statistics

Standard deviation0.9335849129
Coefficient of variation (CV)0.05121212874
Kurtosis0.3956771771
Mean18.22976189
Median Absolute Deviation (MAD)0.5999984741
Skewness0.2816137782
Sum4593.899997
Variance0.8715807896
2020-08-25T00:04:42.147744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
18.79999924187.1%
 
18.5166.3%
 
17.70000076156.0%
 
18.39999962145.6%
 
18.20000076135.2%
 
17.39999962124.8%
 
18.29999924114.4%
 
17.60000038104.0%
 
19104.0%
 
17.2999992493.6%
 
18.1000003893.6%
 
16.8999996293.6%
 
18.7000007683.2%
 
19.1000003872.8%
 
17.7999992462.4%
 
17.1000003862.4%
 
17.8999996262.4%
 
19.2000007662.4%
 
1862.4%
 
18.6000003862.4%
 
16.552.0%
 
19.3999996252.0%
 
19.541.6%
 
1741.6%
 
17.2000007641.6%
 
Other values (19)3313.1%
 
ValueCountFrequency (%) 
15.8000001910.4%
 
16.1000003810.4%
 
16.2999992410.4%
 
16.552.0%
 
16.6000003820.8%
 
16.7000007620.8%
 
16.7999992420.8%
 
16.8999996293.6%
 
1741.6%
 
17.1000003862.4%
 
ValueCountFrequency (%) 
21.3999996220.8%
 
20.8999996210.4%
 
20.3999996210.4%
 
20.2000007610.4%
 
20.1000003820.8%
 
2010.4%
 
19.8999996231.2%
 
19.7999992431.2%
 
19.7000007631.2%
 
19.6000003820.8%
 

target
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count176
Unique (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.150793602305747
Minimum0.0
Maximum47.5
Zeros1
Zeros (%)0.4%
Memory size2.1 KiB
2020-08-25T00:04:42.259689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.054999948
Q112.4749999
median19.20000076
Q325.29999924
95-th percentile32.59999847
Maximum47.5
Range47.5
Interquartile range (IQR)12.82499933

Descriptive statistics

Standard deviation8.36874035
Coefficient of variation (CV)0.4369918304
Kurtosis-0.333811429
Mean19.1507936
Median Absolute Deviation (MAD)6.25
Skewness0.1463530852
Sum4825.999988
Variance70.03581505
2020-08-25T00:04:42.356080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20.3999996241.6%
 
25.7999992431.2%
 
20.531.2%
 
16.531.2%
 
25.2999992431.2%
 
8.80000019131.2%
 
14.8999996231.2%
 
9.39999961931.2%
 
22.1000003831.2%
 
19.2000007631.2%
 
12.3999996231.2%
 
23.6000003831.2%
 
6.59999990520.8%
 
20.1000003820.8%
 
32.9000015320.8%
 
32.5999984720.8%
 
11.8000001920.8%
 
17.7000007620.8%
 
10.8000001920.8%
 
18.1000003820.8%
 
21.2000007620.8%
 
32.2999992420.8%
 
24.8999996220.8%
 
15.1999998120.8%
 
17.3999996220.8%
 
Other values (151)18975.0%
 
ValueCountFrequency (%) 
010.4%
 
0.699999988110.4%
 
310.4%
 
3.70000004820.8%
 
3.90000009510.4%
 
410.4%
 
4.09999990510.4%
 
5.19999980910.4%
 
5.30000019110.4%
 
5.59999990510.4%
 
ValueCountFrequency (%) 
47.510.4%
 
40.0999984710.4%
 
38.0999984710.4%
 
35.2000007610.4%
 
3510.4%
 
34.7999992410.4%
 
34.510.4%
 
34.2999992410.4%
 
33.5999984710.4%
 
32.9000015320.8%
 

Interactions

2020-08-25T00:04:07.764881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:07.884960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:08.019556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:08.292484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:08.414845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:08.547137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:08.672438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:08.809314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:08.944614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:09.076069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:09.204461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:09.331195image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:09.464508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:09.591310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:09.712153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:09.828847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:09.954568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:10.082373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:10.207255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:10.331299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:10.467536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:10.593899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:10.738138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:10.866323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:10.997243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:11.130759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:11.260747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:11.396433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:11.523991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:11.655588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:11.782169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:11.903642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:12.185962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:12.297210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:12.411755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:12.534435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:12.650074image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:12.775222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:12.892327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:13.013308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:13.144170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:13.264849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:13.392054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:13.511110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:13.639035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:13.750777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:13.873976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:13.996175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:14.115454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:14.235006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:14.363603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:14.484732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:14.618887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:14.742117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:14.866646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:14.996628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:15.129279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:15.262669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:15.389388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:15.508173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:15.624568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:15.929637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:16.066454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:16.196537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:16.326517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:16.464336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:16.597596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:16.740806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:16.873067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:17.014233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:17.154696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:17.290852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:17.434596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:17.567779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:17.698381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:17.823811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:17.952871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:18.077175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:18.192910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:18.309862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:18.441554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:18.566377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:18.699611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:18.822456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:04:19.845799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:19.986839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:04:22.193957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:22.315611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:22.439283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:04:22.828493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:04:23.215011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:04:23.789666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:23.914025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:24.033290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:24.164567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:24.297947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:04:25.500190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:04:25.917686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:26.042352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:04:26.832065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:04:27.829979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:27.962075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:28.089483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:28.214820image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:28.345104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:28.479492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:28.607384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:28.738014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:28.876585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:29.009572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:29.155043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:29.285760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:29.423629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:29.558323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:29.693048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:29.834575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:29.966995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:30.094558image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:30.216796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:30.352975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:30.492118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:30.624562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:30.760743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:30.914259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:31.048439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:31.196490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:31.340020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:31.644757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:31.786062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:31.929653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:32.080417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:32.220759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:32.355790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:32.489426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:32.614938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:32.742759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:32.867850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:33.010996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:33.149951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:33.274063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:33.412065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:33.541926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:33.674331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:33.805064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:33.935894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:34.075868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:34.203798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:34.327662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:34.447601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:34.571599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:34.694046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:34.808870image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:34.926404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:35.054311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:35.172949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:35.457287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:35.579824image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:35.702169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:35.832231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:35.961987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:36.092882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:36.216424image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:36.335174image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:36.460515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:36.576542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:04:36.801620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:36.911889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:37.029578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:37.146153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:37.274121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:37.392113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:37.516169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:37.638229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:37.755743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:37.881414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:37.999696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:38.120003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:04:42.484339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:04:42.753955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:04:43.021260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:04:43.295307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:04:38.358409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:04:38.693888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

DensityAgeWeightHeightNeckChestAbdomenHipThighKneeAnkleBicepsForearmWristtarget
01.070823.0154.2567.7536.20000193.09999885.19999794.50000059.00000037.29999921.90000032.00000027.40000017.10000012.300000
11.085322.0173.2572.2538.50000093.59999883.00000098.69999758.70000137.29999923.40000030.50000028.90000018.2000016.100000
21.041422.0154.0066.2534.00000095.80000387.90000299.19999759.59999838.90000224.00000028.79999925.20000116.60000025.299999
31.075126.0184.7572.2537.400002101.80000386.400002101.19999760.09999837.29999922.79999932.40000229.40000018.20000110.400000
41.034024.0184.2571.2534.40000297.300003100.000000101.90000263.20000142.20000124.00000032.20000127.70000117.70000128.700001
51.050224.0210.2574.7539.000000104.50000094.400002107.80000366.00000042.00000025.60000035.70000130.60000018.79999920.900000
61.054926.0181.0069.7536.400002105.09999890.699997100.30000358.40000238.29999922.90000031.90000027.79999917.70000119.200001
71.070425.0176.0072.5037.79999999.59999888.50000097.09999860.00000039.40000223.20000130.50000029.00000018.79999912.400000
81.090025.0191.0074.0038.099998100.90000282.50000099.90000262.90000238.29999923.79999935.90000231.10000018.2000014.100000
91.072223.0198.2573.5042.09999899.59999888.599998104.09999863.09999841.70000125.00000035.59999830.00000019.20000111.700000

Last rows

DensityAgeWeightHeightNeckChestAbdomenHipThighKneeAnkleBicepsForearmWristtarget
2421.030466.0234.2572.0041.400002119.699997109.000000109.09999863.70000142.40000224.60000035.59999830.70000119.50000030.400000
2431.025667.0227.7572.7541.299999115.800003113.400002109.80000365.59999846.00000025.40000035.29999929.79999919.50000032.599998
2441.033467.0199.5068.5040.700001118.300003106.099998101.59999858.20000138.79999924.10000032.09999829.29999918.50000029.000000
2451.064168.0155.5069.2536.29999997.40000284.30000394.40000254.29999937.50000022.60000029.20000127.29999918.50000015.200000
2461.030869.0215.5070.5040.799999113.699997107.599998110.00000063.29999944.00000022.60000037.50000032.59999818.79999930.200001
2471.073670.0134.2567.0034.90000289.19999783.59999888.80000349.59999834.79999921.50000025.60000025.70000118.50000011.000000
2481.023672.0201.0069.7540.900002108.500000105.000000104.50000059.59999840.79999923.20000135.20000128.60000020.10000033.599998
2491.032872.0186.7566.0038.900002111.099998111.500000101.69999760.29999937.29999921.50000031.29999927.20000118.00000029.299999
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